Abstract: In syntactic pattern recognition a pattern can be
represented by a graph. Given an unknown pattern represented by
a graph g, the problem of recognition is to determine if the graph g
belongs to a language L(G) generated by a graph grammar G. The
so-called IE graphs have been defined in [1] for a description of
patterns. The IE graphs are generated by so-called ETPL(k) graph
grammars defined in [1]. An efficient, parsing algorithm for ETPL(k)
graph grammars for syntactic recognition of patterns represented by
IE graphs has been presented in [1]. In practice, structural
descriptions may contain pattern distortions, so that the assignment
of a graph g, representing an unknown pattern, to
a graph language L(G) generated by an ETPL(k) graph grammar G is
rejected by the ETPL(k) type parsing. Therefore, there is a need for
constructing effective parsing algorithms for recognition of distorted
patterns. The purpose of this paper is to present a new approach to
syntactic recognition of distorted patterns. To take into account all
variations of a distorted pattern under study, a probabilistic
description of the pattern is needed. A random IE graph approach is
proposed here for such a description ([2]).
Abstract: The Minimum Vertex Cover (MVC) problem is a classic
graph optimization NP - complete problem. In this paper a competent
algorithm, called Vertex Support Algorithm (VSA), is designed to
find the smallest vertex cover of a graph. The VSA is tested on a
large number of random graphs and DIMACS benchmark graphs.
Comparative study of this algorithm with the other existing methods
has been carried out. Extensive simulation results show that the VSA
can yield better solutions than other existing algorithms found in the
literature for solving the minimum vertex cover problem.